疾病模型LSTM

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import numpy as np
from pandas import read_csv
from matplotlib import pyplot as plt
import math
from keras.models import Sequential
from keras.layers import Dense
from sklearn.preprocessing import MinMaxScaler
from keras.layers import LSTM
from sklearn.metrics import mean_squared_error

data = read_csv('C:\\Users\\50515\\Desktop\\PythonTest\\传染病按医疗机构按日数据.csv')
data = data.iloc[:,:][data.ORGCODE==330784002]
data = data.sort_values(by='TONGJIRQ')
data = data['BINGLISHU']
data = data.values.astype('float32')
data = np.array(data).reshape(-1,1)
scaler=MinMaxScaler()
dataset=scaler.fit_transform(data)#归一化

seed = 7
np.random.seed(seed)
batch_size = 1
epochs = 40
look_back=3
hidden_layer_num=4

def create_data0(data,look_back=3,splitsize=0.7):
    """perceptron_model训练的数据导入"""
    datax,datay=[],[]
    for i in range(len(data)-look_back-1):
        x=data[i:i+look_back,0]#每次去look_back个数据
        datax.append(x)
        y=data[i+look_back,0]#如果x是0,1,2，则y是3
        datay.append(y)
    return datax,datay

def create_data(data,look_back=3,splitsize=0.7):
    """
    给定当前时间t,预测t+1，使用数据（t-2,t-1,t）
    :param data:数据
    :param look_back:输出数据集的格式，默认3[1,2,3],如果改成4则为[1,2,3,4]
    :return: (x_train,y_train),(x_test,y_test)：实际数据，结果,训练数据，结果
    """
    datax,datay=[],[]
    for i in range(len(data)-look_back-1):
        x=data[i:i+look_back,0]#每次去look_back个数据
        datax